How to use Python NumPy arange() Function

Python Numpy arange() function is used to create an array with regularly spaced values within a specified range. It is similar to the built-in range() function but returns a NumPy array. This function takes four parameters start, stop, step, dtype. The arange() and Python range() contain the same similarities like syntax and parameters but range() works only with integers, it doesn’t support the float type whereas arrange() supports both int and float types.

In this article, I will explain the NumPy arange() function using its syntax, parameters, and how to create a NumPy array in several ways with examples. For more examples of NumPy, refer to  NumPy Tutorial.

1. Quick Examples of NumPy arange() Function

If you are in a hurry, below are some quick examples of how to use Python NumPy arange() function.

# Quick examples of numpy arange() function

# Example 1: Use arange() with single argument
#  Generate array 
arr = np.arange(9)

# Example 2: Get the array 
# Use arange() function  
arr = np.arange(1, 12)

# Example 3: Get the array Use arange()
# with custom step-size
arr = np.arange(3, 22, 4)

# Example 4: Custom step-size
arr = np.arange(start=2, stop=22, step=3)

# Example 5: Get the array with float values
arr = np.arange(1.5, 5.5, 0.5)

# Example 6: Get the array with negatives
arr = np.arange(-13, -2)

# Example 8: Providing negative arguments
arr = np.arange(-13, -2, 3)

2. Syntax of NumPy arange()

Following is the syntax of the numpy.arange() function.

# Syntax of arange()
numpy.arange([start, ]stop, [step, ]dtype=None)

2.2 Parameters of arange()

Following are the parameters of the arange() function.

• start – This is an optional parameter used to specify the start of the interval. Its default value is 0. It includes in the interval.
• stop – This parameter is used to specify the end of the interval. The interval does not include this value.
• step – This is an optional parameter indicating the step size of the interval. Its default value is 1.
• dtype – This Parameter is optional and controls the data type of the resultant Numpy array. The default value of this parameter is None.

2.3 Return Value

It returns a ndarray of evenly spaced values. If the array returns floating-point elements the array’s length will be ceil((stop-start)/step).

Note: arange() never includes the stop number in its result.

3. Usage NumPy arange() Function

The NumPy library has various numeric and mathematical tools to operate on multi-dimensional arrays and matrices. Using arange() you can create a ndarray is one of the fundamental tools in NumPy. ndarray is obtained certain numerical ranges using the numpy.arange() function. You can define the interval of the values contained in an array, space between them, and their type with four parameters of arange().

The main difference between range() and arange() is that range is a built-in Python class, while arange() is a function that belongs to a third-party library (NumPy).

When you use numpy.arange() with a single argument, it creates an array with values ranging from 0 to the specified value (exclusive) with a default step of 1. For instance, numpy.arange(9) generates an array with values from 0 to 8 (inclusive), as the default starting value is 0, and the default step is 1.

# Import numpy
import numpy as np
 
# Use numpy.arange() with single argument
# Generate the array
arr = np.arange(9)
print("Array:",arr)

Yields below output.

4. Use numpy.arange() Function to Generate NumPy Array

You can use the numpy.arange() function to generate an array with values from 1 to 11 (12 exclusive). For example, numpy.arange(1, 12) generates an array with values from 1 to 11 (inclusive), as you specified the starting value as 1, and the default step is 1. The resulting array contains integers from 1 to 11.

# Use numpy.arange() function
# Get the array  
arr = np.arange(1, 12)
print("Array:",arr)

# Output:
# Array: [ 1  2  3  4  5  6  7  8  9 10 11]

5. Use arange() Function to Custom Step-Size

You can use numpy.arange() to create an array with a custom step size. For example, numpy.arange(3, 22, 4) generates an array starting from 3 and ending at (but not including) 22, with a step size of 4. The resulting array contains values 3, 7, 11, 15, and 19, which are generated by incrementing the previous value by the specified step size. This demonstrates how you can use numpy.arange() to create arrays with custom step sizes.

# Use numpy.arange() 
# To custom step-size
arr = np.arange(3, 22, 4)
print("Array:",arr)

# Output:
# Array: [ 3  7 11 15 19]

# Custom step-size
arr = np.arange(start=2, stop=22, step=3)
print("Array:",arr)

# Output:
# Array: [ 2  5  8 11 14 17 20]

6. Get Array with Float values using arange()

Alternatively, you can use numpy.arange() to generate an array with float values. For instance, numpy.arange(1.0, 5.0, 0.5) generates an array with values starting from 1.0 and ending at (but not including) 5.0, with a step size of 0.5. The resulting array contains floating-point numbers with increments of 0.5. Adjust the start, stop, and step values as needed for your specific use case.

# Get the array with float values
arr = np.arange(1.5, 5.5, 0.5)
print("Array with float values:\n",arr)

# Output:
# Array with float values:
#  [1.5 2.  2.5 3.  3.5 4.  4.5 5. ]

7. Use Negative Arguments

Similarly, you can also use numpy.arange() to create an array with negative values. For example, numpy.arange(-13, -2) generates an array with values starting from -13 and ending at (but not including) -2, with a default step size of 1. The resulting array contains negative integers within the specified range. Adjust the start, stop, and step values as needed for your specific use case.

# Get the array with negatives values
arr = np.arange(-13, -2)
print("Array with negatives values:\n",arr)

# Output:
# Array with negatives values:
#  [-13 -12 -11 -10  -9  -8  -7  -6  -5  -4  -3]

# Providing negative arguments
arr = np.arange(-13, -2, 3)
print("Array with negatives values:\n",arr)

# Output:
# Array with negatives values:
# [-13 -10  -7  -4]

Frequently Asked Questions

Conclusion

In this article, I have explained how to use the NumPy arange() function and using this how you can create an array with specified intervals with examples.

Happy Learning!!

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Reference

• https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace